Distributionally Robust Learning
DOWNLOAD
Download Distributionally Robust Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Distributionally Robust Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Distributionally Robust Learning
DOWNLOAD
Author : Ruidi Chen
language : en
Publisher:
Release Date : 2020-12-23
Distributionally Robust Learning written by Ruidi Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Mathematics categories.
Distributionally Robust Learning
DOWNLOAD
Author : Ruidi Chen
language : en
Publisher:
Release Date : 2020
Distributionally Robust Learning written by Ruidi Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic books categories.
This monograph provides insight into a technique that has gained a lot of recent interest in developing robust supervised learning solutions that are founded in sound mathematical principles. It will be enlightening for researchers, practitioners and students in the optimization of machine learning systems.
Wasserstein Distributionally Robust Learning
DOWNLOAD
Author : OROOSH Shafieezadeh Abadeh
language : en
Publisher:
Release Date : 2020
Wasserstein Distributionally Robust Learning written by OROOSH Shafieezadeh Abadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Mots-clés de l'auteur: Distributionally robust optimization ; Wasserstein distance ; Regularization ; Supervised Learning ; Inverse optimization ; Kalman filter ; Frank-Wolfe algorithm.
Distributionally Robust Optimization And Its Applications In Machine Learning
DOWNLOAD
Author : Yang Kang
language : en
Publisher:
Release Date : 2017
Distributionally Robust Optimization And Its Applications In Machine Learning written by Yang Kang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Optimal transport costs include as a special case the so-called Wasserstein distance, which is popular in various statistical applications. The use of optimal transport costs is advantageous relative to the use of divergence-based formulations because the region of distributional uncertainty contains distributions which explore samples outside of the support of the empirical measure, therefore explaining why many machine learning algorithms have the ability to improve generalization. Moreover, the DRO representations that we use to unify the previously mentioned machine learning algorithms, provide a clear interpretation of the so-called regularization parameter, which is known to play a crucial role in controlling generalization error. As we establish, the regularization parameter corresponds exactly to the size of the distributional uncertainty region. Another contribution of this dissertation is the development of statistical methodology to study data-driven DRO formulations based on optimal transport costs.
Distributionally Robust Unsupervised Domain Adaptation And Its Applications In 2d And 3d Image Analysis
DOWNLOAD
Author : Yibin Wang
language : en
Publisher:
Release Date : 2023
Distributionally Robust Unsupervised Domain Adaptation And Its Applications In 2d And 3d Image Analysis written by Yibin Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Obtaining ground-truth label information from real-world data along with uncertainty quantification can be challenging or even infeasible. In the absence of labeled data for a certain task, unsupervised domain adaptation (UDA) techniques have shown great accomplishment by learning transferable knowledge from labeled source domain data and adapting it to unlabeled target domain data, yet uncertainties are still a big concern under domain shifts. Distributionally robust learning (DRL) is emerging as a high-potential technique for building reliable learning systems that are robust to distribution shifts. In this research, a distributionally robust unsupervised domain adaptation (DRUDA) method is proposed to enhance the machine learning model generalization ability under input space perturbations. The DRL-based UDA learning scheme is formulated as a min-max optimization problem by optimizing worst-case perturbations of the training source data. Our Wasserstein distributionally robust framework can reduce the shifts in the joint distributions across domains. The proposed DRUDA method has been tested on various benchmark datasets. In addition, a gradient mapping-guided explainable network (GMGENet) is proposed to analyze 3D medical images for extracapsular extension (ECE) identification. DRUDA-enhanced GMGENet is evaluated, and experimental results demonstrate that the proposed DRUDA improves transfer performance on target domains for the 3D image analysis task successfully. This research enhances the understanding of distributionally robust optimization in domain adaptation and is expected to advance the current unsupervised machine learning techniques.
Reliable Machine Learning Via Distributional Robustness
DOWNLOAD
Author : Hongseok Namkoong
language : en
Publisher:
Release Date : 2019
Reliable Machine Learning Via Distributional Robustness written by Hongseok Namkoong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
As machine learning systems increasingly get applied in high-stake domains such as autonomous vehicles and medical diagnosis, it is imperative that they maintain good performance when deployed. Modeling assumptions rarely hold due to noisy inputs, shifts in environment, unmeasured confounders, and even adversarial attacks to the system. The standard machine learning paradigm that optimize average performance is brittle to even small amounts of noise, and exhibit poor performance on underrepresented minority groups. We study \emph{distributionally robust} learning procedures that explicitly protect against potential shifts in the data-generating distribution. Instead of doing well just on average, distributionally robust methods learn models that can do well on a range of scenarios that are different to the training distribution. In the first part of thesis, we show that robustness to small perturbations in the data allows better generalization by optimally trading between approximation and estimation error. We show that robust solutions provide asymptotically exact confidence intervals and finite-sample guarantees for stochastic optimization problems. In the second part of the thesis, we focus on notions of distributional robustness that correspond to uniform performance across different subpopulations. We build procedures that balance tail-performance alongside classical notions of average performance. To trade these multiple goals \emph{optimally}, we show fundamental trade-offs (lower bounds), and develop efficient procedures that achieve these limits (upper bounds). Then, we extend our formulation to study partial covariate shifts, where we are interested in marginal distributional shifts on a subset of the feature vector. We provide convex procedures for these robust formulations, and characterize their non-asymptotic convergence properties. In the final part of the thesis, we develop and analyze distributionally robust approaches using Wasserstein distances, which allows models to generalize to distributions that have different support than the training distribution. We show that for smooth neural networks, our robust procedure guarantees performance under imperceptible adversarial perturbations. Extending such notions to protect against distributions defined on learned feature spaces, we show these models can also improve performance across unseen domains.
Distributionally Robust Optimization And Its Applications In Mathematical Finance Statistics And Reinforcement Learning
DOWNLOAD
Author : Zhengqing Zhou
language : en
Publisher:
Release Date : 2021
Distributionally Robust Optimization And Its Applications In Mathematical Finance Statistics And Reinforcement Learning written by Zhengqing Zhou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Distributionally robust optimization (DRO) is a zero-sum game between a decision-maker and an adversarial player. The decision-maker aims to minimize the expected loss, while the adversarial player wishes the loss to be maximized by replacing the underlying probability measure with another measure within a distributional uncertainty set. DRO has emerged as an important paradigm for machine learning, statistics, and operations research. DRO produces powerful insights in terms of statistical interpretability, performance guarantees, and parameter tuning. In this thesis, we apply DRO to three different topics: martingale optimal transport, convex regression, and offline reinforcement learning. We show how the DRO formulations/techniques improve the existing results in the literature.
Distributionally Robust Performance Analysis
DOWNLOAD
Author : Fei He
language : en
Publisher:
Release Date : 2018
Distributionally Robust Performance Analysis written by Fei He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
We explain our procedure in the context of classification, which is of substantial importance in machine learning applications.
Data Analysis And Applications 3
DOWNLOAD
Author : Andreas Makrides
language : en
Publisher: John Wiley & Sons
Release Date : 2020-03-31
Data Analysis And Applications 3 written by Andreas Makrides and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-31 with Business & Economics categories.
Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
Robust Optimization
DOWNLOAD
Author : Aharon Ben-Tal
language : en
Publisher: Princeton University Press
Release Date : 2009-08-10
Robust Optimization written by Aharon Ben-Tal and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-10 with Mathematics categories.
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.